A technique for real-time detecting, locating, and quantifying damage in large polymer composite structures made of carbon fibers and carbon nanotube networks
Bibliographic record
Abstract
A significant safety concern preventing extensive use of composite materials for large polymer composite structures is the ability to detect, locate, and quantify damages that occur at one or several locations in large polymer composite structures. Real-time health monitoring of large polymer composite structures improves their performance, durability, and reliability while minimizing the life cycle cost. In this article, we present a new, practical, and real-time structural health monitoring technique for detecting, locating, and quantifying damages in large polymer composite structures made of carbon fibers and carbon nanotube networks. In this technique, electrically conductive epoxy resin was prepared by dispersing multiwalled carbon nanotubes into epoxy matrix. This modified epoxy matrix was then incorporated with long carbon fibers to make large composite plates. Two sets of grid points made from silver-epoxy paste were mounted on the surface of the large plates. The first set was used to apply the constant electric current, and the second set was utilized to measure the electric potential. The electric potentials across the second set of grid points on the undamaged plate were measured and used as a reference set. Two different damages were created by drilling holes and by applying impact loading on the large plates. It is found that the electric potential between the contact points surrounding the damage changes. The significant change in electric potential corresponds to the damage location in the plates. As such, drilled holes, impact damages, and barely visible impact damages are detected, located, and quantified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".